Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, deep learning approaches are vulnerable to adversarial attacks, which, in a crowd-counting context, can lead to serious security issues. However, attack and defense mechanisms have been virtually unexplored in regression tasks, let alone for crowd density estimation. In this paper, we investigate the effectiveness of existing attack strategies on crowd-counting networks, and introduce a simple yet effective pixel-wise detection mechanism. It builds on the intuition that, when attacking a multitask network, in our case estimating crowd density and scene depth, both outputs will be perturbed, and thus the second one can be used for detection purposes. We will demonstrate that this significantly outperforms heuristic and uncertainty-based strategies.
Code (0)
등록된 구현이 없습니다.
Tasks
Crowd CountingDensity EstimationSimilar Papers 제목 키워드 기반
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression Tasks
Pixel-wise regression tasks (e.g., monocular depth estimation (MDE) and optical flow estimation (OFE)) have been widely involved in our daily life in applications like autonomous driving, augmented reality and video comp…
Adversarial RobustnessAutonomous DrivingDepth EstimationMonocular Depth Estimation+2Attacking Important Pixels for Anchor-free Detectors
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change the prediction result. Existing adversarial attacks on object detection focus on attacking an…
Adversarial Attackobject-detectionObject DetectionPose EstimationOn the Adversarial Robustness of Camera-based 3D Object Detection
In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness of these methods to adversarial attacks h…
3D Object DetectionAdversarial AttackAdversarial RobustnessAutonomous Driving+4Image Masking for Robust Self-Supervised Monocular Depth Estimation
Self-supervised monocular depth estimation is a salient task for 3D scene understanding. Learned jointly with monocular ego-motion estimation, several methods have been proposed to predict accurate pixel-wise depth witho…
Autonomous DrivingDepth EstimationMonocular Depth EstimationMotion Estimation+1Sparse and Imperceivable Adversarial Attacks
Neural networks have been proven to be vulnerable to a variety of adversarial attacks. From a safety perspective, highly sparse adversarial attacks are particularly dangerous. On the other hand the pixelwise perturbation…